Lexical Semantic Change detection, i.e., the task of identifying words that change meaning over time, is a very active research area, with applications in NLP, lexicography, and linguistics. Evaluation is currently the most pressing problem in Lexical Semantic Change detection, as no gold standards are available to the community, which hinders progress. We present the results of the first shared task that addresses this gap by providing researchers with an evaluation framework and manually annotated, high-quality datasets for English, German, Latin, and Swedish. 33 teams submitted 186 systems, which were evaluated on two subtasks.
@article{arxiv.2007.11464,
title = {SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection},
author = {Dominik Schlechtweg and Barbara McGillivray and Simon Hengchen and Haim Dubossarsky and Nina Tahmasebi},
journal= {arXiv preprint arXiv:2007.11464},
year = {2020}
}